NEWS.md

In n3d: Speaker Diarization with 'Nemotron 3 Diarization' in R 'torch'

n3d 0.1.0

• First CRAN release.

n3d 0.0.1.1

• On CUDA, encoder layers run as one traced TorchScript function shared by all 31 layers, and each chunk ends with a minor garbage collection. Peak device memory drops from 1.7-2.1 GiB to 0.47-0.55 GiB (weights are 0.37 GiB), and a streaming step from 55 ms to about 18 ms on an RTX 5060 Ti. load_n3d() traces and warms up the layer once (about 1 s). options(n3d.jit = FALSE) runs the plain R functions; the CPU always does.
• Log-mel features and logits for the whole input stay on the CPU, and one chunk at a time moves to the device, so device memory no longer grows with the length of the audio (0.52 GiB for 9.8 min).

n3d 0.0.1

• Initial version: native R 'torch' port of NVIDIA Nemotron 3 Diarization with offline diarization (diarize()), streaming sessions (n3d_stream(), n3d_stream_push(), n3d_stream_finish()), log-mel features and the arrival-order speaker cache implemented in R, and pinned-revision weight download through 'hfhub'.

n3d documentation built on Oct. 14, 2026, 5:08 p.m.